Design of feature selection algorithm for high-dimensional network data based on supervised discriminant projection.

ABSTRACT: High dimension and complexity of network high-dimensional data lead to poor feature selection effect network high-dimensional data. To effectively solve this problem, feature selection algorithms for high-dimensional network data based on supervised discriminant projection (SDP) have been designed. The sparse representation problem of high-dimensional network data is transformed into an Lp norm optimization problem, and the sparse subspace clustering method is used to cluster high-dimensional network data. Dimensionless processing is carried out for the clustering processing results. Based on the linear projection matrix and the best transformation matrix, the dimensionless processing results are reduced by combining the SDP. The sparse constraint method is used to achieve feature selectio

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC10319262 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature